Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations38576
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.1 MiB
Average record size in memory871.5 B

Variable types

Numeric9
Categorical10
DateTime4

Alerts

application_type has constant value "INDIVIDUAL"Constant
grade is highly overall correlated with int_rate and 1 other fieldsHigh correlation
id is highly overall correlated with member_idHigh correlation
installment is highly overall correlated with loan_amount and 1 other fieldsHigh correlation
int_rate is highly overall correlated with grade and 1 other fieldsHigh correlation
loan_amount is highly overall correlated with installment and 1 other fieldsHigh correlation
member_id is highly overall correlated with idHigh correlation
sub_grade is highly overall correlated with grade and 1 other fieldsHigh correlation
total_payment is highly overall correlated with installment and 1 other fieldsHigh correlation
loan_status is highly imbalanced (52.0%)Imbalance
annual_income is highly skewed (γ1 = 31.074172)Skewed
id has unique valuesUnique
member_id has unique valuesUnique

Reproduction

Analysis started2024-09-09 20:06:22.627662
Analysis finished2024-09-09 20:06:36.312194
Duration13.68 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681037.06
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:36.441241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile371014
Q1513517
median662728
Q3836506
95-th percentile1040057.2
Maximum1077501
Range1022767
Interquartile range (IQR)322989

Descriptive statistics

Standard deviation211324.58
Coefficient of variation (CV)0.31029821
Kurtosis-0.73078462
Mean681037.06
Median Absolute Deviation (MAD)160285
Skewness0.086652826
Sum2.6271686 × 1010
Variance4.4658077 × 1010
MonotonicityNot monotonic
2024-09-10T01:36:36.577193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1077430 1
 
< 0.1%
317833 1
 
< 0.1%
477433 1
 
< 0.1%
478263 1
 
< 0.1%
443318 1
 
< 0.1%
548105 1
 
< 0.1%
720941 1
 
< 0.1%
640319 1
 
< 0.1%
366380 1
 
< 0.1%
454773 1
 
< 0.1%
Other values (38566) 38566
> 99.9%
ValueCountFrequency (%)
54734 1
< 0.1%
55742 1
< 0.1%
57245 1
< 0.1%
57416 1
< 0.1%
58915 1
< 0.1%
59006 1
< 0.1%
61390 1
< 0.1%
61419 1
< 0.1%
62102 1
< 0.1%
65426 1
< 0.1%
ValueCountFrequency (%)
1077501 1
< 0.1%
1077430 1
< 0.1%
1077175 1
< 0.1%
1076863 1
< 0.1%
1075358 1
< 0.1%
1075269 1
< 0.1%
1072053 1
< 0.1%
1071795 1
< 0.1%
1071570 1
< 0.1%
1070078 1
< 0.1%

address_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
CA
6894 
NY
3701 
FL
2773 
TX
2664 
NJ
 
1822
Other values (45)
20722 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77152
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGA
2nd rowCA
3rd rowCA
4th rowTX
5th rowIL

Common Values

ValueCountFrequency (%)
CA 6894
17.9%
NY 3701
 
9.6%
FL 2773
 
7.2%
TX 2664
 
6.9%
NJ 1822
 
4.7%
IL 1486
 
3.9%
PA 1482
 
3.8%
VA 1375
 
3.6%
GA 1355
 
3.5%
MA 1310
 
3.4%
Other values (40) 13714
35.6%

Length

2024-09-10T01:36:36.698833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6894
17.9%
ny 3701
 
9.6%
fl 2773
 
7.2%
tx 2664
 
6.9%
nj 1822
 
4.7%
il 1486
 
3.9%
pa 1482
 
3.8%
va 1375
 
3.6%
ga 1355
 
3.5%
ma 1310
 
3.4%
Other values (40) 13714
35.6%

Most occurring characters

ValueCountFrequency (%)
A 15231
19.7%
C 9831
12.7%
N 7731
10.0%
L 5117
 
6.6%
M 4558
 
5.9%
Y 4100
 
5.3%
T 3796
 
4.9%
O 3347
 
4.3%
I 3003
 
3.9%
F 2773
 
3.6%
Other values (14) 17665
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77152
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15231
19.7%
C 9831
12.7%
N 7731
10.0%
L 5117
 
6.6%
M 4558
 
5.9%
Y 4100
 
5.3%
T 3796
 
4.9%
O 3347
 
4.3%
I 3003
 
3.9%
F 2773
 
3.6%
Other values (14) 17665
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 77152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15231
19.7%
C 9831
12.7%
N 7731
10.0%
L 5117
 
6.6%
M 4558
 
5.9%
Y 4100
 
5.3%
T 3796
 
4.9%
O 3347
 
4.3%
I 3003
 
3.9%
F 2773
 
3.6%
Other values (14) 17665
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15231
19.7%
C 9831
12.7%
N 7731
10.0%
L 5117
 
6.6%
M 4558
 
5.9%
Y 4100
 
5.3%
T 3796
 
4.9%
O 3347
 
4.3%
I 3003
 
3.9%
F 2773
 
3.6%
Other values (14) 17665
22.9%

application_type
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
INDIVIDUAL
38576 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters385760
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINDIVIDUAL
2nd rowINDIVIDUAL
3rd rowINDIVIDUAL
4th rowINDIVIDUAL
5th rowINDIVIDUAL

Common Values

ValueCountFrequency (%)
INDIVIDUAL 38576
100.0%

Length

2024-09-10T01:36:36.807389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:36.898896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
individual 38576
100.0%

Most occurring characters

ValueCountFrequency (%)
I 115728
30.0%
D 77152
20.0%
N 38576
 
10.0%
V 38576
 
10.0%
U 38576
 
10.0%
A 38576
 
10.0%
L 38576
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 385760
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 115728
30.0%
D 77152
20.0%
N 38576
 
10.0%
V 38576
 
10.0%
U 38576
 
10.0%
A 38576
 
10.0%
L 38576
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 385760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 115728
30.0%
D 77152
20.0%
N 38576
 
10.0%
V 38576
 
10.0%
U 38576
 
10.0%
A 38576
 
10.0%
L 38576
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 115728
30.0%
D 77152
20.0%
N 38576
 
10.0%
V 38576
 
10.0%
U 38576
 
10.0%
A 38576
 
10.0%
L 38576
 
10.0%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
10+ years
8870 
< 1 year
4575 
2 years
4382 
3 years
4088 
4 years
3428 
Other values (6)
13233 

Length

Max length9
Median length7
Mean length7.4947636
Min length6

Characters and Unicode

Total characters289118
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row< 1 year
2nd row9 years
3rd row4 years
4th row< 1 year
5th row10+ years

Common Values

ValueCountFrequency (%)
10+ years 8870
23.0%
< 1 year 4575
11.9%
2 years 4382
11.4%
3 years 4088
10.6%
4 years 3428
 
8.9%
5 years 3273
 
8.5%
1 year 3229
 
8.4%
6 years 2228
 
5.8%
7 years 1772
 
4.6%
8 years 1476
 
3.8%

Length

2024-09-10T01:36:37.003321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 30772
37.7%
10 8870
 
10.9%
1 7804
 
9.5%
year 7804
 
9.5%
4575
 
5.6%
2 4382
 
5.4%
3 4088
 
5.0%
4 3428
 
4.2%
5 3273
 
4.0%
6 2228
 
2.7%
Other values (3) 4503
 
5.5%

Most occurring characters

ValueCountFrequency (%)
43151
14.9%
y 38576
13.3%
e 38576
13.3%
a 38576
13.3%
r 38576
13.3%
s 30772
10.6%
1 16674
 
5.8%
0 8870
 
3.1%
+ 8870
 
3.1%
< 4575
 
1.6%
Other values (8) 21902
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185076
64.0%
Decimal Number 47446
 
16.4%
Space Separator 43151
 
14.9%
Math Symbol 13445
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16674
35.1%
0 8870
18.7%
2 4382
 
9.2%
3 4088
 
8.6%
4 3428
 
7.2%
5 3273
 
6.9%
6 2228
 
4.7%
7 1772
 
3.7%
8 1476
 
3.1%
9 1255
 
2.6%
Lowercase Letter
ValueCountFrequency (%)
y 38576
20.8%
e 38576
20.8%
a 38576
20.8%
r 38576
20.8%
s 30772
16.6%
Math Symbol
ValueCountFrequency (%)
+ 8870
66.0%
< 4575
34.0%
Space Separator
ValueCountFrequency (%)
43151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185076
64.0%
Common 104042
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
43151
41.5%
1 16674
 
16.0%
0 8870
 
8.5%
+ 8870
 
8.5%
< 4575
 
4.4%
2 4382
 
4.2%
3 4088
 
3.9%
4 3428
 
3.3%
5 3273
 
3.1%
6 2228
 
2.1%
Other values (3) 4503
 
4.3%
Latin
ValueCountFrequency (%)
y 38576
20.8%
e 38576
20.8%
a 38576
20.8%
r 38576
20.8%
s 30772
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 289118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43151
14.9%
y 38576
13.3%
e 38576
13.3%
a 38576
13.3%
r 38576
13.3%
s 30772
10.6%
1 16674
 
5.8%
0 8870
 
3.1%
+ 8870
 
3.1%
< 4575
 
1.6%
Other values (8) 21902
7.6%

grade
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
B
11674 
A
9689 
C
7904 
D
5182 
E
2786 
Other values (2)
1341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38576
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowE
3rd rowC
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%

Length

2024-09-10T01:36:37.116567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:37.228695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
b 11674
30.3%
a 9689
25.1%
c 7904
20.5%
d 5182
13.4%
e 2786
 
7.2%
f 1028
 
2.7%
g 313
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38576
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 38576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
RENT
18439 
MORTGAGE
17198 
OWN
2838 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length5
Mean length5.7122563
Min length3

Characters and Unicode

Total characters220356
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowMORTGAGE
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT 18439
47.8%
MORTGAGE 17198
44.6%
OWN 2838
 
7.4%
OTHER 98
 
0.3%
NONE 3
 
< 0.1%

Length

2024-09-10T01:36:37.359131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:37.462995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 18439
47.8%
mortgage 17198
44.6%
own 2838
 
7.4%
other 98
 
0.3%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 35738
16.2%
R 35735
16.2%
T 35735
16.2%
G 34396
15.6%
N 21283
9.7%
O 20137
9.1%
M 17198
7.8%
A 17198
7.8%
W 2838
 
1.3%
H 98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 220356
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 35738
16.2%
R 35735
16.2%
T 35735
16.2%
G 34396
15.6%
N 21283
9.7%
O 20137
9.1%
M 17198
7.8%
A 17198
7.8%
W 2838
 
1.3%
H 98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 220356
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 35738
16.2%
R 35735
16.2%
T 35735
16.2%
G 34396
15.6%
N 21283
9.7%
O 20137
9.1%
M 17198
7.8%
A 17198
7.8%
W 2838
 
1.3%
H 98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 35738
16.2%
R 35735
16.2%
T 35735
16.2%
G 34396
15.6%
N 21283
9.7%
O 20137
9.1%
M 17198
7.8%
A 17198
7.8%
W 2838
 
1.3%
H 98
 
< 0.1%
Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size301.5 KiB
Minimum2021-01-01 00:00:00
Maximum2021-12-12 00:00:00
2024-09-10T01:36:37.593079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:37.739813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct107
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size301.5 KiB
Minimum2021-01-13 00:00:00
Maximum2022-01-20 00:00:00
2024-09-10T01:36:37.879461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:38.021686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct102
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size301.5 KiB
Minimum2021-01-13 00:00:00
Maximum2021-12-15 00:00:00
2024-09-10T01:36:38.160098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:38.300938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loan_status
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Fully Paid
32145 
Charged Off
5333 
Current
 
1098

Length

Max length11
Median length10
Mean length10.052857
Min length7

Characters and Unicode

Total characters387799
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharged Off
2nd rowFully Paid
3rd rowCharged Off
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 32145
83.3%
Charged Off 5333
 
13.8%
Current 1098
 
2.8%

Length

2024-09-10T01:36:38.450707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:38.562866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
fully 32145
42.3%
paid 32145
42.3%
charged 5333
 
7.0%
off 5333
 
7.0%
current 1098
 
1.4%

Most occurring characters

ValueCountFrequency (%)
l 64290
16.6%
37478
9.7%
a 37478
9.7%
d 37478
9.7%
u 33243
8.6%
F 32145
8.3%
y 32145
8.3%
P 32145
8.3%
i 32145
8.3%
f 10666
 
2.8%
Other values (8) 38586
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 274267
70.7%
Uppercase Letter 76054
 
19.6%
Space Separator 37478
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 64290
23.4%
a 37478
13.7%
d 37478
13.7%
u 33243
12.1%
y 32145
11.7%
i 32145
11.7%
f 10666
 
3.9%
r 7529
 
2.7%
e 6431
 
2.3%
g 5333
 
1.9%
Other values (3) 7529
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
F 32145
42.3%
P 32145
42.3%
C 6431
 
8.5%
O 5333
 
7.0%
Space Separator
ValueCountFrequency (%)
37478
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 350321
90.3%
Common 37478
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 64290
18.4%
a 37478
10.7%
d 37478
10.7%
u 33243
9.5%
F 32145
9.2%
y 32145
9.2%
P 32145
9.2%
i 32145
9.2%
f 10666
 
3.0%
r 7529
 
2.1%
Other values (7) 31057
8.9%
Common
ValueCountFrequency (%)
37478
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 387799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 64290
16.6%
37478
9.7%
a 37478
9.7%
d 37478
9.7%
u 33243
8.6%
F 32145
8.3%
y 32145
8.3%
P 32145
8.3%
i 32145
8.3%
f 10666
 
2.8%
Other values (8) 38586
9.9%
Distinct102
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size301.5 KiB
Minimum2021-02-13 00:00:00
Maximum2022-12-01 00:00:00
2024-09-10T01:36:38.677623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:38.813369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

member_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean847651.51
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:38.947949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile385926.5
Q1662978.75
median847356.5
Q31045652.5
95-th percentile1269618.5
Maximum1314167
Range1243468
Interquartile range (IQR)382673.75

Descriptive statistics

Standard deviation266810.46
Coefficient of variation (CV)0.31476433
Kurtosis-0.5759103
Mean847651.51
Median Absolute Deviation (MAD)192255.5
Skewness-0.20399343
Sum3.2699005 × 1010
Variance7.118782 × 1010
MonotonicityNot monotonic
2024-09-10T01:36:39.089324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1314167 1
 
< 0.1%
317830 1
 
< 0.1%
605588 1
 
< 0.1%
607168 1
 
< 0.1%
539366 1
 
< 0.1%
706707 1
 
< 0.1%
915440 1
 
< 0.1%
819682 1
 
< 0.1%
377345 1
 
< 0.1%
563479 1
 
< 0.1%
Other values (38566) 38566
> 99.9%
ValueCountFrequency (%)
70699 1
< 0.1%
73673 1
< 0.1%
74724 1
< 0.1%
76583 1
< 0.1%
80353 1
< 0.1%
80364 1
< 0.1%
84914 1
< 0.1%
85483 1
< 0.1%
86999 1
< 0.1%
89243 1
< 0.1%
ValueCountFrequency (%)
1314167 1
< 0.1%
1313524 1
< 0.1%
1311748 1
< 0.1%
1311441 1
< 0.1%
1306957 1
< 0.1%
1306721 1
< 0.1%
1305201 1
< 0.1%
1305008 1
< 0.1%
1304956 1
< 0.1%
1304884 1
< 0.1%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Debt consolidation
18214 
credit card
4998 
other
3824 
home improvement
2876 
major purchase
2110 
Other values (9)
6554 

Length

Max length18
Median length16
Mean length13.764906
Min length3

Characters and Unicode

Total characters530995
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcar
2nd rowcar
3rd rowcar
4th rowcar
5th rowcar

Common Values

ValueCountFrequency (%)
Debt consolidation 18214
47.2%
credit card 4998
 
13.0%
other 3824
 
9.9%
home improvement 2876
 
7.5%
major purchase 2110
 
5.5%
small business 1776
 
4.6%
car 1497
 
3.9%
wedding 928
 
2.4%
medical 667
 
1.7%
moving 559
 
1.4%
Other values (4) 1127
 
2.9%

Length

2024-09-10T01:36:39.234622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt 18214
26.6%
consolidation 18214
26.6%
credit 4998
 
7.3%
card 4998
 
7.3%
other 3824
 
5.6%
home 2876
 
4.2%
improvement 2876
 
4.2%
major 2110
 
3.1%
purchase 2110
 
3.1%
business 1776
 
2.6%
Other values (9) 6554
 
9.6%

Most occurring characters

ValueCountFrequency (%)
o 67920
12.8%
i 48899
 
9.2%
t 48793
 
9.2%
n 43422
 
8.2%
e 42296
 
8.0%
c 33151
 
6.2%
a 32800
 
6.2%
d 31048
 
5.8%
29974
 
5.6%
s 27794
 
5.2%
Other values (14) 124898
23.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 482713
90.9%
Space Separator 29974
 
5.6%
Uppercase Letter 18214
 
3.4%
Connector Punctuation 94
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 67920
14.1%
i 48899
10.1%
t 48793
10.1%
n 43422
9.0%
e 42296
8.8%
c 33151
 
6.9%
a 32800
 
6.8%
d 31048
 
6.4%
s 27794
 
5.8%
l 22842
 
4.7%
Other values (11) 83748
17.3%
Space Separator
ValueCountFrequency (%)
29974
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 18214
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 500927
94.3%
Common 30068
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 67920
13.6%
i 48899
9.8%
t 48793
9.7%
n 43422
8.7%
e 42296
8.4%
c 33151
 
6.6%
a 32800
 
6.5%
d 31048
 
6.2%
s 27794
 
5.5%
l 22842
 
4.6%
Other values (12) 101962
20.4%
Common
ValueCountFrequency (%)
29974
99.7%
_ 94
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 530995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 67920
12.8%
i 48899
 
9.2%
t 48793
 
9.2%
n 43422
 
8.2%
e 42296
 
8.0%
c 33151
 
6.2%
a 32800
 
6.2%
d 31048
 
5.8%
29974
 
5.6%
s 27794
 
5.2%
Other values (14) 124898
23.5%

sub_grade
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
B3
2834 
A4
2803 
A5
2654 
B5
2644 
B4
 
2455
Other values (30)
25186 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77152
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC4
2nd rowE1
3rd rowC5
4th rowB2
5th rowA1

Common Values

ValueCountFrequency (%)
B3 2834
 
7.3%
A4 2803
 
7.3%
A5 2654
 
6.9%
B5 2644
 
6.9%
B4 2455
 
6.4%
C1 2089
 
5.4%
B2 1990
 
5.2%
C2 1972
 
5.1%
B1 1751
 
4.5%
A3 1740
 
4.5%
Other values (25) 15644
40.6%

Length

2024-09-10T01:36:39.348449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b3 2834
 
7.3%
a4 2803
 
7.3%
a5 2654
 
6.9%
b5 2644
 
6.9%
b4 2455
 
6.4%
c1 2089
 
5.4%
b2 1990
 
5.2%
c2 1972
 
5.1%
b1 1751
 
4.5%
a3 1740
 
4.5%
Other values (25) 15644
40.6%

Most occurring characters

ValueCountFrequency (%)
B 11674
15.1%
A 9689
12.6%
4 8087
10.5%
3 7976
10.3%
C 7904
10.2%
5 7855
10.2%
2 7677
10.0%
1 6981
9.0%
D 5182
6.7%
E 2786
 
3.6%
Other values (2) 1341
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38576
50.0%
Decimal Number 38576
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 8087
21.0%
3 7976
20.7%
5 7855
20.4%
2 7677
19.9%
1 6981
18.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 38576
50.0%
Common 38576
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11674
30.3%
A 9689
25.1%
C 7904
20.5%
D 5182
13.4%
E 2786
 
7.2%
F 1028
 
2.7%
G 313
 
0.8%
Common
ValueCountFrequency (%)
4 8087
21.0%
3 7976
20.7%
5 7855
20.4%
2 7677
19.9%
1 6981
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11674
15.1%
A 9689
12.6%
4 8087
10.5%
3 7976
10.3%
C 7904
10.2%
5 7855
10.2%
2 7677
10.0%
1 6981
9.0%
D 5182
6.7%
E 2786
 
3.6%
Other values (2) 1341
 
1.7%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
36 months
28237 
60 months
10339 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters385760
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 60 months
2nd row 36 months
3rd row 36 months
4th row 60 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 28237
73.2%
60 months 10339
 
26.8%

Length

2024-09-10T01:36:39.456182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:39.549061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
months 38576
50.0%
36 28237
36.6%
60 10339
 
13.4%

Most occurring characters

ValueCountFrequency (%)
77152
20.0%
6 38576
10.0%
m 38576
10.0%
o 38576
10.0%
n 38576
10.0%
t 38576
10.0%
h 38576
10.0%
s 38576
10.0%
3 28237
 
7.3%
0 10339
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231456
60.0%
Space Separator 77152
 
20.0%
Decimal Number 77152
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 38576
16.7%
o 38576
16.7%
n 38576
16.7%
t 38576
16.7%
h 38576
16.7%
s 38576
16.7%
Decimal Number
ValueCountFrequency (%)
6 38576
50.0%
3 28237
36.6%
0 10339
 
13.4%
Space Separator
ValueCountFrequency (%)
77152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231456
60.0%
Common 154304
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 38576
16.7%
o 38576
16.7%
n 38576
16.7%
t 38576
16.7%
h 38576
16.7%
s 38576
16.7%
Common
ValueCountFrequency (%)
77152
50.0%
6 38576
25.0%
3 28237
 
18.3%
0 10339
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77152
20.0%
6 38576
10.0%
m 38576
10.0%
o 38576
10.0%
n 38576
10.0%
t 38576
10.0%
h 38576
10.0%
s 38576
10.0%
3 28237
 
7.3%
0 10339
 
2.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
Not Verified
16464 
Verified
12335 
Source Verified
9777 

Length

Max length15
Median length12
Mean length11.48131
Min length8

Characters and Unicode

Total characters442903
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSource Verified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowVerified

Common Values

ValueCountFrequency (%)
Not Verified 16464
42.7%
Verified 12335
32.0%
Source Verified 9777
25.3%

Length

2024-09-10T01:36:39.663454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-10T01:36:39.775143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
verified 38576
59.5%
not 16464
25.4%
source 9777
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 86929
19.6%
i 77152
17.4%
r 48353
10.9%
V 38576
8.7%
f 38576
8.7%
d 38576
8.7%
o 26241
 
5.9%
26241
 
5.9%
N 16464
 
3.7%
t 16464
 
3.7%
Other values (3) 29331
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 351845
79.4%
Uppercase Letter 64817
 
14.6%
Space Separator 26241
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 86929
24.7%
i 77152
21.9%
r 48353
13.7%
f 38576
11.0%
d 38576
11.0%
o 26241
 
7.5%
t 16464
 
4.7%
u 9777
 
2.8%
c 9777
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 38576
59.5%
N 16464
25.4%
S 9777
 
15.1%
Space Separator
ValueCountFrequency (%)
26241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 416662
94.1%
Common 26241
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 86929
20.9%
i 77152
18.5%
r 48353
11.6%
V 38576
9.3%
f 38576
9.3%
d 38576
9.3%
o 26241
 
6.3%
N 16464
 
4.0%
t 16464
 
4.0%
S 9777
 
2.3%
Other values (2) 19554
 
4.7%
Common
ValueCountFrequency (%)
26241
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 86929
19.6%
i 77152
17.4%
r 48353
10.9%
V 38576
8.7%
f 38576
8.7%
d 38576
8.7%
o 26241
 
5.9%
26241
 
5.9%
N 16464
 
3.7%
t 16464
 
3.7%
Other values (3) 29331
 
6.6%

annual_income
Real number (ℝ)

SKEWED 

Distinct5096
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69644.54
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:39.898900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q141500
median60000
Q383200.5
95-th percentile144000
Maximum6000000
Range5996000
Interquartile range (IQR)41700.5

Descriptive statistics

Standard deviation64293.681
Coefficient of variation (CV)0.92316901
Kurtosis2296.4627
Mean69644.54
Median Absolute Deviation (MAD)20000
Skewness31.074172
Sum2.6866078 × 109
Variance4.1336774 × 109
MonotonicityNot monotonic
2024-09-10T01:36:40.039249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1471
 
3.8%
50000 1033
 
2.7%
40000 859
 
2.2%
45000 810
 
2.1%
75000 802
 
2.1%
65000 794
 
2.1%
30000 790
 
2.0%
70000 721
 
1.9%
48000 697
 
1.8%
80000 651
 
1.7%
Other values (5086) 29948
77.6%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4080 1
 
< 0.1%
4200 1
 
< 0.1%
4800 3
< 0.1%
4888 1
 
< 0.1%
5000 1
 
< 0.1%
5500 1
 
< 0.1%
6000 5
< 0.1%
7000 1
 
< 0.1%
7200 3
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 1
 
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 4
< 0.1%
1176000 1
 
< 0.1%

dti
Real number (ℝ)

Distinct2863
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13327433
Minimum0
Maximum0.2999
Zeros173
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:40.180692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0215
Q10.0821
median0.1342
Q30.1859
95-th percentile0.2383
Maximum0.2999
Range0.2999
Interquartile range (IQR)0.1038

Descriptive statistics

Standard deviation0.066661553
Coefficient of variation (CV)0.50018299
Kurtosis-0.84842441
Mean0.13327433
Median Absolute Deviation (MAD)0.0519
Skewness-0.029921136
Sum5141.1906
Variance0.0044437627
MonotonicityNot monotonic
2024-09-10T01:36:40.318596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 173
 
0.4%
0.12 48
 
0.1%
0.18 44
 
0.1%
0.192 40
 
0.1%
0.168 38
 
0.1%
0.132 38
 
0.1%
0.1248 38
 
0.1%
0.1429 36
 
0.1%
0.135 35
 
0.1%
0.15 35
 
0.1%
Other values (2853) 38051
98.6%
ValueCountFrequency (%)
0 173
0.4%
0.0001 3
 
< 0.1%
0.0002 5
 
< 0.1%
0.0003 2
 
< 0.1%
0.0004 3
 
< 0.1%
0.0005 1
 
< 0.1%
0.0006 1
 
< 0.1%
0.0007 5
 
< 0.1%
0.0008 5
 
< 0.1%
0.0009 3
 
< 0.1%
ValueCountFrequency (%)
0.2999 1
 
< 0.1%
0.2995 1
 
< 0.1%
0.2993 3
< 0.1%
0.2992 2
< 0.1%
0.2989 1
 
< 0.1%
0.2988 1
 
< 0.1%
0.2986 1
 
< 0.1%
0.2985 1
 
< 0.1%
0.2983 1
 
< 0.1%
0.2982 1
 
< 0.1%

installment
Real number (ℝ)

HIGH CORRELATION 

Distinct15132
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.86297
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:40.457103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile73.68
Q1168.45
median283.045
Q3434.4425
95-th percentile767.6725
Maximum1305.19
Range1289.5
Interquartile range (IQR)265.9925

Descriptive statistics

Standard deviation209.092
Coefficient of variation (CV)0.63969315
Kurtosis1.2221012
Mean326.86297
Median Absolute Deviation (MAD)123.615
Skewness1.1204475
Sum12609066
Variance43719.465
MonotonicityNot monotonic
2024-09-10T01:36:40.602309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 68
 
0.2%
311.02 54
 
0.1%
180.96 53
 
0.1%
150.8 46
 
0.1%
368.45 45
 
0.1%
372.12 44
 
0.1%
330.76 42
 
0.1%
339.31 42
 
0.1%
186.61 41
 
0.1%
317.72 41
 
0.1%
Other values (15122) 38100
98.8%
ValueCountFrequency (%)
15.69 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.74 1
< 0.1%
21.81 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 4
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

int_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct371
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12048831
Minimum0.0542
Maximum0.2459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:40.740878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0542
5-th percentile0.0639
Q10.0932
median0.1186
Q30.1459
95-th percentile0.1862
Maximum0.2459
Range0.1917
Interquartile range (IQR)0.0527

Descriptive statistics

Standard deviation0.037164121
Coefficient of variation (CV)0.30844586
Kurtosis-0.43809842
Mean0.12048831
Median Absolute Deviation (MAD)0.0261
Skewness0.29226418
Sum4647.9572
Variance0.0013811719
MonotonicityNot monotonic
2024-09-10T01:36:41.186164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1099 932
 
2.4%
0.1349 811
 
2.1%
0.1149 796
 
2.1%
0.0751 755
 
2.0%
0.0788 701
 
1.8%
0.0749 633
 
1.6%
0.1171 589
 
1.5%
0.0999 583
 
1.5%
0.079 559
 
1.4%
0.0542 524
 
1.4%
Other values (361) 31693
82.2%
ValueCountFrequency (%)
0.0542 524
1.4%
0.0579 390
1.0%
0.0599 328
0.9%
0.06 18
 
< 0.1%
0.0603 413
1.1%
0.0617 238
0.6%
0.0639 54
 
0.1%
0.0654 303
0.8%
0.0662 376
1.0%
0.0676 159
 
0.4%
ValueCountFrequency (%)
0.2459 1
 
< 0.1%
0.244 1
 
< 0.1%
0.2411 3
 
< 0.1%
0.2391 11
< 0.1%
0.2359 4
 
< 0.1%
0.2352 9
< 0.1%
0.2322 9
< 0.1%
0.2313 9
< 0.1%
0.2294 2
 
< 0.1%
0.2285 8
< 0.1%

loan_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct880
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11296.067
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:41.331749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15500
median10000
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9500

Descriptive statistics

Standard deviation7460.746
Coefficient of variation (CV)0.6604729
Kurtosis0.74696983
Mean11296.067
Median Absolute Deviation (MAD)5000
Skewness1.0507752
Sum4.3575708 × 108
Variance55662731
MonotonicityNot monotonic
2024-09-10T01:36:41.468778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2761
 
7.2%
12000 2295
 
5.9%
5000 1977
 
5.1%
15000 1860
 
4.8%
6000 1852
 
4.8%
20000 1597
 
4.1%
8000 1538
 
4.0%
25000 1369
 
3.5%
4000 1090
 
2.8%
7000 989
 
2.6%
Other values (870) 21248
55.1%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 267
0.7%
1050 4
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 668
1.7%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34200 1
 
< 0.1%
34000 15
 
< 0.1%
33950 7
 
< 0.1%
33600 6
 
< 0.1%
33500 2
 
< 0.1%

total_acc
Real number (ℝ)

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.132544
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:41.610339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q114
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.392282
Coefficient of variation (CV)0.51472991
Kurtosis0.68497835
Mean22.132544
Median Absolute Deviation (MAD)7
Skewness0.82314059
Sum853785
Variance129.78409
MonotonicityNot monotonic
2024-09-10T01:36:41.738820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1435
 
3.7%
15 1420
 
3.7%
17 1408
 
3.6%
14 1405
 
3.6%
20 1398
 
3.6%
18 1386
 
3.6%
21 1364
 
3.5%
13 1340
 
3.5%
19 1310
 
3.4%
12 1283
 
3.3%
Other values (72) 24827
64.4%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 172
 
0.4%
4 401
 
1.0%
5 531
1.4%
6 655
1.7%
7 802
2.1%
8 973
2.5%
9 1040
2.7%
10 1140
3.0%
11 1225
3.2%
ValueCountFrequency (%)
90 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
75 2
< 0.1%
74 1
< 0.1%

total_payment
Real number (ℝ)

HIGH CORRELATION 

Distinct19525
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12263.349
Minimum34
Maximum58564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size301.5 KiB
2024-09-10T01:36:41.866547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile1991.75
Q15633
median10042
Q316658
95-th percentile30320.25
Maximum58564
Range58530
Interquartile range (IQR)11025

Descriptive statistics

Standard deviation9051.1048
Coefficient of variation (CV)0.73806145
Kurtosis1.9611767
Mean12263.349
Median Absolute Deviation (MAD)5051
Skewness1.3338282
Sum4.7307093 × 108
Variance81922498
MonotonicityNot monotonic
2024-09-10T01:36:42.005766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6515 32
 
0.1%
11200 31
 
0.1%
11197 26
 
0.1%
6720 25
 
0.1%
5600 23
 
0.1%
10918 22
 
0.1%
11164 22
 
0.1%
14289 22
 
0.1%
6718 20
 
0.1%
13264 20
 
0.1%
Other values (19515) 38333
99.4%
ValueCountFrequency (%)
34 1
< 0.1%
36 1
< 0.1%
67 1
< 0.1%
70 2
< 0.1%
79 1
< 0.1%
84 1
< 0.1%
92 1
< 0.1%
97 1
< 0.1%
99 1
< 0.1%
103 1
< 0.1%
ValueCountFrequency (%)
58564 1
< 0.1%
58480 1
< 0.1%
57835 1
< 0.1%
56849 1
< 0.1%
56663 1
< 0.1%
56199 1
< 0.1%
55907 1
< 0.1%
55769 1
< 0.1%
55139 1
< 0.1%
55106 1
< 0.1%

Interactions

2024-09-10T01:36:34.469427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:26.058129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.625004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.514855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.410395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.562663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.621377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.633231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.584729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.564036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:26.187792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.714633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.607493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.505873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.702667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.748001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.727886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.670112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.663130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:26.281287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.808987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.703013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.605467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.811366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.853241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.828851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.765089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.761946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.037515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.907863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.799522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.705428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.924691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.960857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.932411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.859189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.864040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.130163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.007162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.900208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.805819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.047597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.075995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.060017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.957162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.963226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.226558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.106248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.001035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.935424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.155842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.196758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.169555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.070019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:35.070864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.330754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.212954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.107764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.072875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.292679image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.318776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.282471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.186569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:35.455409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.431036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.316926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.211508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.232001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.413343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.427449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.389397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.289134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:35.543604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:27.521903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:28.407275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:29.305657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:30.403736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:31.519650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:32.525681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:33.484978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-10T01:36:34.373719image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-10T01:36:42.120529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
address_stateannual_incomedtiemp_lengthgradehome_ownershipidinstallmentint_rateloan_amountloan_statusmember_idpurposesub_gradetermtotal_acctotal_paymentverification_status
address_state1.0000.0160.0330.0230.0140.1350.0910.0150.0180.0200.0410.0990.0320.0070.0530.0360.0150.040
annual_income0.0161.000-0.1080.0060.0000.0000.0510.4170.0560.4240.0000.0500.0000.0000.0000.4290.4010.000
dti0.033-0.1081.0000.0190.0630.0240.0870.0660.1210.0740.0450.0870.0820.0570.0850.2400.0630.071
emp_length0.0230.0060.0191.0000.0170.1320.0560.0450.0240.0540.0410.0560.0360.0180.1160.0740.0470.084
grade0.0140.0000.0630.0171.0000.0470.0490.1360.7040.1370.1600.0510.0681.0000.4430.0490.1500.144
home_ownership0.1350.0000.0240.1320.0471.0000.0830.0690.0520.0870.0310.0820.1250.0520.1110.1690.0760.077
id0.0910.0510.0870.0560.0490.0831.0000.0800.0490.1170.1490.9990.0730.0680.3040.0530.1090.237
installment0.0150.4170.0660.0450.1360.0690.0801.0000.2490.9570.0600.0800.1130.1220.1470.2450.8730.276
int_rate0.0180.0560.1210.0240.7040.0520.0490.2491.0000.2530.1840.0490.0590.6770.475-0.0680.2340.167
loan_amount0.0200.4240.0740.0540.1370.0870.1170.9570.2531.0000.1100.1160.1160.1240.3630.2720.8880.318
loan_status0.0410.0000.0450.0410.1600.0310.1490.0600.1840.1101.0000.1540.0730.1670.3290.0300.2340.070
member_id0.0990.0500.0870.0560.0510.0820.9990.0800.0490.1160.1541.0000.0750.0700.3160.0530.1080.254
purpose0.0320.0000.0820.0360.0680.1250.0730.1130.0590.1160.0730.0751.0000.0530.1150.0480.1000.097
sub_grade0.0070.0000.0570.0181.0000.0520.0680.1220.6770.1240.1670.0700.0531.0000.4780.0500.1340.152
term0.0530.0000.0850.1160.4430.1110.3040.1470.4750.3630.3290.3160.1150.4781.0000.1000.3350.261
total_acc0.0360.4290.2400.0740.0490.1690.0530.245-0.0680.2720.0300.0530.0480.0500.1001.0000.2380.102
total_payment0.0150.4010.0630.0470.1500.0760.1090.8730.2340.8880.2340.1080.1000.1340.3350.2381.0000.287
verification_status0.0400.0000.0710.0840.1440.0770.2370.2760.1670.3180.0700.2540.0970.1520.2610.1020.2871.000

Missing values

2024-09-10T01:36:35.710214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-10T01:36:36.066971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idaddress_stateapplication_typeemp_lengthgradehome_ownershipissue_datelast_credit_pull_datelast_payment_dateloan_statusnext_payment_datemember_idpurposesub_gradetermverification_statusannual_incomedtiinstallmentint_rateloan_amounttotal_acctotal_payment
01077430GAINDIVIDUAL< 1 yearCRENT11-02-202113-09-202113-04-2021Charged Off13-05-20211314167carC460 monthsSource Verified30000.00.010059.830.1527250041009
11072053CAINDIVIDUAL9 yearsERENT01-01-202114-12-202115-01-2021Fully Paid15-02-20211288686carE136 monthsSource Verified48000.00.0535109.430.1864300043939
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